日本における化石燃料と経済成長の因果性分析 - 多変数アプローチに基づいて石田葉月 Discussion Paper April 2011 Graduate School of Economics and Osaka School of International Public Pol

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1 Discussion Papers In Economics And Business 日本における化石燃料と経済成長の因果性分析 - 多変数アプローチに基づいて石田葉月 Discussion Paper Graduate School of Economics and Osaka School of International Public Policy (OSIPP) Osaka University, Toyonaka, Osaka , JAPAN

2 日本における化石燃料と経済成長の因果性分析 - 多変数アプローチに基づいて石田葉月 Discussion Paper April 2011 Graduate School of Economics and Osaka School of International Public Policy (OSIPP) Osaka University, Toyonaka, Osaka , JAPAN

3 日本における化石燃料と経済成長の因果性分析 - 多変数アプローチに基づいて石田葉月要旨 : 化石燃料は特異な天然資源であり他のエネルギー源との代替は限定的であるしたがって化石燃料と経済がどのように結びついているかを知ることは持続型社会のあるべき姿を議論するうえで極めて重要であると思われるエネルギーと経済成長の因果関係についての既存研究は多いが化石燃料と経済成長の因果関係を検討したものは少ないまた既存研究の多くは 2 変数による時系列分析を行っている本研究では日本を事例として非化石エネルギーと労働を加えた多変数の時系列分析により化石燃料の消費量と GDP の Granger 因果性を明らかにしたその結果化石燃料から GDP に対する一方向の因果性が見出されたこのことは化石燃料は経済成長の原動力であり化石燃料の供給減は経済成長を阻害することを示唆しているまた非化石エネルギーは化石燃料の代わりとはならず化石燃料依存から脱却するためには経済成長路線そのものを見直す必要があると結論づけた JFL Classification: Q32, Q43, Q57 Key Words: 化石燃料, 経済成長, 共和分, Granger 因果性本稿を作成するにあたり大槻恒裕先生 ( 大阪大学大学院国際公共政策研究科 ) のご支援を頂きましたここに感謝の意を表します福島大学共生システム理工学類, 福島県福島市金谷川 1 電話 &FAX: , e059@ipc.fukushima-u.ac.jp

4 1. (Soddy, 1912; Georgescu-Roegen, 1971) Georgescu-Roegen (1971) GDP( GNP) GDP GDP GDP Kraft and Kraft (1978) GNP Abosedra and Baghestani (1989) Yu and Hwang (1984) Yu and Choi (1985) GNP Stern (1993, 2000) GDP GDP (Lee, 2006; Mozumder and Marathe, 2007; Chiou-Wei et al., 2008) Erol and Yu (1988) Zachariadis (2007) 1

5 GNP Soytas and Sari (2003) GDP Lee (2006) GDP GDP 1 GDP GDP GDP ( Lee and Chang (2005); Zou and Chau (2006); Zamani (2007); Yuan et al. (2008)) 1970 GDP Granger 1970 (Figure 1) GDP GDP 2

6 GDP GDP (Stern, 1993, 2000) (Stern, 1993, 2000; Ghali and El-Sakka, 2004; Soytas and Sari, 2007; Yuan et al., 2008) Daly (1996) Georgescu-Roegen (1971) Soddy (1926) Georgescu-Roegen (1971) GDP Figure 1: Primary energy supply in Japan (1970=100) 3

7 2. GDP GDP (Stern, 1993, 2000) 2 GDP FMR(Field Metabolic Rate) (Ayres and Ayres, 1999) 100 Augmented Dickey-Fuller (ADF) (Dickey and Fuller, 1979) Phillips-Perron (PP) (Phillips and Perron, 1988) (Engle and Granger, 1987) Eq.(1) VAR (Vector Autoregressive) Johansen and Juselius (1990) X t = µ + p Π i X t i + ϵ t (1) i=1 X t µ Π i ϵ t Granger (1988) 2 4

8 VECM(Vector Error Correction Model) GDP Granger VECM Eq.(1) X t 1 p 1 X t = µ + Γ i X t i + ΠX t 1 + ϵ t (2) i=1 1 Π = p i=1 Π i I Γ i = p j=i+1 Π j I GDP(Y) GDP (F) (NF) (L) 3. Table 1 Augumented Dickey-Fuller (ADF) Table 2 Phillips-Perron (PP) I(1) Table 1: Augmented Dickey-Fuller unit root tests Variables With a time trend Without a time trend Y (2) (2) F (0) (0) NF (0) (0) L (0) (0) Y (1)** F (0)** NF (2)** L (1)* Lag lengths(in parenthesis) are determined by AIC. * Significant at the 5% level. ** Significant at the 1% level. Johansen and Juselius (1990) Table 3 GDP GDP GDP GDP 5

9 Table 2: Phillips-Perron unit root tests Variables With a time trend Without a time trend Y F NF L Y * F ** NF ** L * * Significant at the 5% level. ** Significant at the 1% level. Table 3: Maximum likelihood cointegration tests Cointegration rank Trace test Maximum Eigenvalue test Statistics Critical value a Statistics Critical value a None At most At most At most Normalized cointegration equation Y 1.292( 2.199)F (4.329)NF ( 5.967)L 0.008( 0.938)T REND a 95% Critical value The lag structure of VAR is determined by AIC. T-values are given in parentheses. GDP Table 4 ECM Table 5 Granger GDP GDP GDP GDP GDP Table 5 GDP GDP GDP GDP GDP Granger 6

10 Table 4: Estimates of error correction models Variables Y F NF L Constant (-2.025)* (-0.025) (-2.729)* (-0.418) Y(-1) 0.082(0.284) 0.187(0.277) (-0.780) 0.166(0.511) Y(-2) (-2.190)* (-1.323) (-1.216) Y(-3) (0.749) F(-1) (-0.987) (-0.372) (-1.043) (-1.517) F(-2) (-1.195) NF(-1) 0.101(2.417)* (-0.292) 0.029(0.178) 0.066(1.517) NF(-2) (-1.026) (-0.431) - NF(-3) (-1.825) - NF(-4) (1.071) - L(-1) 0.143(0.586) 0.675(1.215) 0.252(0.229) 0.248(0.634) L(-2) (-2.591)* (-0.276) L(-3) (0.661) (-0.388) L(-4) (-2.005) ECT(-1) (-2.029)* (-0.027) (-2.728)** (-0.413) Diagnostic test R R F-statistic DW ARCH BG Lag lengths are determined by AIC. T-statistics are in parenthesis. * Significant at the 5% level. ** Significant at the 1% level. VAR Figure 2 Figure 3 2 Figure 2 GDP ECM GDP GDP ECM GDP GDP GDP (Figure 3) GDP GDP ECM ECM GDP 7

11 Table 5: Granger causality test results Dependent F-statistics T-statistics variables Short-run Long-run Y F NF L ECT t 1 Y * * F NF ** L * Significant at the 5% level. ** Significant at the 1% level. Shock in F Shock in NF Shock in L Figure 2: Impulse responses of GDP to generalized one standard deviation shock 4. Granger 8

12 Response of F Response of NF Response of L Figure 3: Impulse responses to generalized one standard deviation shock in GDP GDP GDP (Japanese Atomic Energy Commission, 2005) 1970 (Figure 1) GDP (government failure) GDP 9

13 (Trainer, 2007) (Soddy, 1912; Georgescu-Roegen, 1971) (E.On Netz, 2005) (Trainer, 2007) (Trainer, 2007) (Ferguson, 2008) in-out 100m 10

14 (Testart, 1982) ERoEI(Energy Return on Energy Invested) ERoEI 2 (Hammerschlag, 2006) GDP I(1) VECM Granger GDP GDP GDP Granger GDP GDP GDP References Abosedra, S. and Baghestani, H. (1989). New evidence on the causal relationship between U.S. energy consumption and gross national product. Journal of Energy and Development, 14(2): Ayres, R. U. and Ayres, L. W. (1999). Accounting for Resources, 2. Edward Elgar. Chiou-Wei, S. Z., Chen, C., and Zhu, Z. (2008). Economic growth and energy consumption - evidence from linear and nonlinear Granger causality. Energy Economics, 30: Daly, H. E. (1996). Beyond Growth. Beacon Press. 11

15 Dickey, D. A. and Fuller, W. A. (1979). Distribution of the estimation for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(386): Engle, R. F. and Granger, C. W. J. (1987). Cointegration and error correction: representation, estimation and testing. Econometrica, 55: E.On Netz (2005). Wind Report Erol, U. and Yu, E. S. H. (1988). On the causal relationship between energy and income for industrialized countries. Journal of Energy and Development, 13: Ferguson, A. R. B. (2008). Wind power: Benefits and limitations. In Pimentel, D., editor, Biofuels, Solar and Wind as Renewable Energy Systems, chapter 6, pages Springer, The Netherlands. Georgescu-Roegen, N. (1971). The Entropy Law and the Economic Process. Harvard University Press, Cambridge, MA. Ghali, K. H. and El-Sakka, M. I. T. (2004). Energy use and output growth in Canada: a multivariate cointegration analysis. Energy Economics, 26: Granger, C. W. J. (1988). Some recent developments in a concept of causality. Journal of Econometrics, 39: Hammerschlag, R. (2006). Ethanol s energy return on investment: A survey of the literature 1990-present. Environmental Science and Technology, 40(6): Japanese Atomic Energy Commission (2005). Framework for Nuclear Energy Policy. Johansen, S. and Juselius, K. (1990). Maximum likelihood estimation and inference on cointegration -with applications to the demand for money. Oxford Bulletin of Economics and Statistics, 52(2): Kraft, J. and Kraft, A. (1978). On the relationship between energy and gnp. Journal of Energy Development, 3: Lee, C. (2006). The causality relationship between energy consumption and GDP in G-11 countries revisited. Energy Policy, 34: Lee, C. and Chang, C. (2005). Structural breaks, energy consumption, and economic growth revisited: Evidence from Taiwan. Energy Economics, 27: Mozumder, P. and Marathe, A. (2007). Causality relationship between electricity consumption and GDP in Bangladesh. Energy Policy, 35: Phillips, P. and Perron, P. (1988). Testing for unit root in time series regression. Biometrica, 75: Soddy, F. (1912). Matter and Energy. Henny Holt and Company. Soddy, F. (1926). Wealth, Virtual Wealth, and Debt. George Allen and Unwin. 12

16 Soytas, U. and Sari, R. (2003). Energy consumption and GDP: causality relationship in G-7 countries and emerging markets. Energy Economics, 25: Soytas, U. and Sari, R. (2007). The relationship between energy and production: Evidence from Turkish manufacturing industry. Energy Economics, 29: Stern, D. I. (1993). Energy and economic growth in the USA. Energy Economics, 15: Stern, D. I. (2000). A multivariate cointegration analysis of the role of energy in the US macroeconomy. Energy Economics, 22: Testart, A. (1982). The significance of food strage among hunter-gatherers: Residence patterns, population densities, and social inequalities. Current Anthropology, 23: Trainer, T. (2007). Renewable Energy Cannot Sustain a Consumer Society. Springer, The Netherlands. Yu, E. S. H. and Choi, J. Y. (1985). The causal relationhip between energy and GNP: an international comparison. Journal of Energy and Development, 10: Yu, E. S. H. and Hwang, B. K. (1984). The relationship between energy consumption and GNP: further results. Energy Economics, 6: Yuan, J., Kang, J., Zhao, C., and Hu, Z. (2008). Energy consumption and economic growth: Evidence from China at both aggregated and disaggregated levels. Energy Economics, 30: Zachariadis, T. (2007). Exploring the relationship between energy use and economic growth with bivariate models: New evidence from G-7 countries. Energy Economics, 29: Zamani, M. (2007). Energy consumption and economic activities in Iran. Energy Economics, 29: Zou, G. and Chau, K. W. (2006). Short- and long-run effects between oil consumption and economic growth in China. Energy Policy, 34:

17 Causal relationship between fossil fuel consumption and economic growth in Japan: a multivariate approach * Hazuki Ishida Abstract: Fossil fuels (oil, coal, gas) are low-entropy natural resources which seem to be indispensable for our economic prosperity. This paper investigates the relationship between fossil fuel consumption and economic growth in Japan, using a multivariate model of fossil fuels, non-fossil energy, labor and GDP. Using the Johansen cointegration technique, the empirical results indicate that there is a long-run relationship among the variables. Then using vector error correction model, the study reveals unidirectional causality running from fossil fuels to GDP. It implies that decline in fossil fuel consumption may hamper economic growth. On the other hand, non-fossil energy use does not appear to have positive effects on economic growth. JEL Classification: Q32, Q43, Q57 Key Words: Fossil fuels, Economic growth, Cointegration, Granger causality * I am grateful to Tsunehiro Otsuki for helpful comments and suggestions. Faculty of Symbiotic Systems Science, Fukushima University, I, Kanayagawa, Fukushima, , Japan. Tel&Fax: +81 (0) , e059@ipc.fukushima-u.ac.jp

2 100 BTU 2

- - 2010 8 14 1 GDP GDP GDP GDP 960-1296 Tel/Fax:024-548-8439 E-mail:e059@ipc.fukushima-u.ac.jp 1 2 100 BTU 2 3 GDP GDP (Stern, 2000) GDP GDP (Cleveland et al., 2000) 4 80 1 10 1 3 (Smil, 2008) 5 IEA(

日本における化石燃料と経済成長の因果性分析 - 多変数アプローチに基づいて 石田葉月 Discussion Paper April 2011 Graduate School of Economics and Osaka School of International Public Pol

日本における化石燃料と経済成長の因果性分析 - 多変数アプローチに基づいて石田葉月 Discussion Paper April 2011 Graduate School of Economics and Osaka School of International Public Pol